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Returns based Bollinger Bands

Bollinger Bands based on Returns

 

This indicator characterizes the price and volatility by providing a channel/band of standard deviations like the Bollinger Bands. In contrary to standard Bollinger Bands which uses price directly, this indicator uses returns due to normalization.

 

The standard Bollinger Bands based on price directly, were one of the first quant or statistical methods for retail traders available. The issue with these bands, standard deviations can only be calculated, if the underlying data is normal distributed. But price (data basis for Bollinger Bands) is log-normal distributed.

This indicator normalizes the underlaying price data to a normal distributed data, calculates the (standard) deviation and transfers the values back to its price domain. With this approach it is from statistical point of view, much more accurate and reflects the actual probabilities much better.

 

Similar as the standard Bollinger Bands, the upper and lower band for this indicator also reflects the limit, where it is unlike for the candle to close outside of this channel/band. If selected multiplier is 1.96 (1.96 Standard deviations), the probability is 95% that it will not close outside. If you choose multiplier 1.0 (1 SD), chances are 68.5% that it will not close outside.

 

Input:

Sample Size: 21 (periods back to calculate returns-based Bollinger Bands)

Multiplier: 1.96 (1.96 standard deviations)



Remark: Indicator is provided for statistical analysis and showing probabilities only and should not be construed as financial advice.


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Percentile of Historical Volatility and Correlation Coefficient shows if the asset is cheap or expensive based on the volatility. It is used to determine a good entry point. It has two indicators built in: Historical Volatility is ranked percentile wise and its correlation to price action which gives an indication of the direction of a possible future move. Together the both indicators can give good entries and direction.   Historical Volatility is a statistical measure of the dispersion of retu
This z-score indicator shows the correct z-score of an asset, as it uses the normalized price data for calculation, which is the only correct way. Z-score is only applicable for normal distributed data, therefore not the actual price is considered, but the normalised returns, which were assumed to follow a normal distribution. Returns are mean reverting and assumed to follow a normal distribution, therefore z-score calculation of returns is more reliable than z-score on price, as price is NOT m
The RSI2.0 indicator uses normalized price data and signal processing steps to get a normal distributed oscillator with no skew (mean is zero). Therefore, it can give much better reads than the traditional RSI.   Areas/Levels of reversal: Overbought or oversold levels from traditional RSI have no statistical significance, therefore the standard deviation bands are implemented here, which can be used in similar way as possible reversal points.   Divergence: As the indicator is nearly normal dist
The Returns Momentum Oscillator (RMO) shows the difference of exponentially weighted volatility. It is used to find market tops and bottoms. Volatility comes in waves, and as the Returns often front run price action it gives directional prediction of market movement.   The Oscillator signal is RMSed (root mean squared) to make the distribution closer to Gaussian distribution. While the traditional RSI indicators are often stuck in overbought or oversold areas for a long time, RMSing of the sign
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